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import os
import glob
import argparse
import logging
import json
import shutil
import subprocess
import numpy as np
from huggingface_hub import hf_hub_download
from scipy.io.wavfile import read
import torch
import re
MATPLOTLIB_FLAG = False
logger = logging.getLogger(__name__)
def download_emo_models(mirror, repo_id, model_name):
if mirror == "openi":
import openi
openi.model.download_model(
"Stardust_minus/Bert-VITS2",
repo_id.split("/")[-1],
"./emotional",
)
else:
hf_hub_download(
repo_id,
"pytorch_model.bin",
local_dir=model_name,
local_dir_use_symlinks=False,
)
def download_checkpoint(
dir_path, repo_config, token=None, regex="G_*.pth", mirror="openi"
):
repo_id = repo_config["repo_id"]
f_list = glob.glob(os.path.join(dir_path, regex))
if f_list:
print("Use existed model, skip downloading.")
return
if mirror.lower() == "openi":
import openi
kwargs = {"token": token} if token else {}
openi.login(**kwargs)
model_image = repo_config["model_image"]
openi.model.download_model(repo_id, model_image, dir_path)
fs = glob.glob(os.path.join(dir_path, model_image, "*.pth"))
for file in fs:
shutil.move(file, dir_path)
shutil.rmtree(os.path.join(dir_path, model_image))
else:
for file in ["DUR_0.pth", "D_0.pth", "G_0.pth"]:
hf_hub_download(
repo_id, file, local_dir=dir_path, local_dir_use_symlinks=False
)
def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False):
assert os.path.isfile(checkpoint_path)
checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
iteration = checkpoint_dict["iteration"]
learning_rate = checkpoint_dict["learning_rate"]
if (
optimizer is not None
and not skip_optimizer
and checkpoint_dict["optimizer"] is not None
):
optimizer.load_state_dict(checkpoint_dict["optimizer"])
elif optimizer is None and not skip_optimizer:
# else: Disable this line if Infer and resume checkpoint,then enable the line upper
new_opt_dict = optimizer.state_dict()
new_opt_dict_params = new_opt_dict["param_groups"][0]["params"]
new_opt_dict["param_groups"] = checkpoint_dict["optimizer"]["param_groups"]
new_opt_dict["param_groups"][0]["params"] = new_opt_dict_params
optimizer.load_state_dict(new_opt_dict)
saved_state_dict = checkpoint_dict["model"]
if hasattr(model, "module"):
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
new_state_dict = {}
for k, v in state_dict.items():
try:
# assert "emb_g" not in k
new_state_dict[k] = saved_state_dict[k]
assert saved_state_dict[k].shape == v.shape, (
saved_state_dict[k].shape,
v.shape,
)
except:
# For upgrading from the old version
if "ja_bert_proj" in k:
v = torch.zeros_like(v)
logger.warn(
f"Seems you are using the old version of the model, the {k} is automatically set to zero for backward compatibility"
)
else:
logger.error(f"{k} is not in the checkpoint")
new_state_dict[k] = v
if hasattr(model, "module"):
model.module.load_state_dict(new_state_dict, strict=False)
else:
model.load_state_dict(new_state_dict, strict=False)
logger.info(
"Loaded checkpoint '{}' (iteration {})".format(checkpoint_path, iteration)
)
return model, optimizer, learning_rate, iteration
def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
logger.info(
"Saving model and optimizer state at iteration {} to {}".format(
iteration, checkpoint_path
)
)
if hasattr(model, "module"):
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
torch.save(
{
"model": state_dict,
"iteration": iteration,
"optimizer": optimizer.state_dict(),
"learning_rate": learning_rate,
},
checkpoint_path,
)
def summarize(
writer,
global_step,
scalars={},
histograms={},
images={},
audios={},
audio_sampling_rate=22050,
):
for k, v in scalars.items():
writer.add_scalar(k, v, global_step)
for k, v in histograms.items():
writer.add_histogram(k, v, global_step)
for k, v in images.items():
writer.add_image(k, v, global_step, dataformats="HWC")
for k, v in audios.items():
writer.add_audio(k, v, global_step, audio_sampling_rate)
def latest_checkpoint_path(dir_path, regex="G_*.pth"):
f_list = glob.glob(os.path.join(dir_path, regex))
f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
x = f_list[-1]
return x
def plot_spectrogram_to_numpy(spectrogram):
global MATPLOTLIB_FLAG
if not MATPLOTLIB_FLAG:
import matplotlib
matplotlib.use("Agg")
MATPLOTLIB_FLAG = True
mpl_logger = logging.getLogger("matplotlib")
mpl_logger.setLevel(logging.WARNING)
import matplotlib.pylab as plt
import numpy as np
fig, ax = plt.subplots(figsize=(10, 2))
im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none")
plt.colorbar(im, ax=ax)
plt.xlabel("Frames")
plt.ylabel("Channels")
plt.tight_layout()
fig.canvas.draw()
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
plt.close()
return data
def plot_alignment_to_numpy(alignment, info=None):
global MATPLOTLIB_FLAG
if not MATPLOTLIB_FLAG:
import matplotlib
matplotlib.use("Agg")
MATPLOTLIB_FLAG = True
mpl_logger = logging.getLogger("matplotlib")
mpl_logger.setLevel(logging.WARNING)
import matplotlib.pylab as plt
import numpy as np
fig, ax = plt.subplots(figsize=(6, 4))
im = ax.imshow(
alignment.transpose(), aspect="auto", origin="lower", interpolation="none"
)
fig.colorbar(im, ax=ax)
xlabel = "Decoder timestep"
if info is not None:
xlabel += "\n\n" + info
plt.xlabel(xlabel)
plt.ylabel("Encoder timestep")
plt.tight_layout()
fig.canvas.draw()
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
plt.close()
return data
def load_wav_to_torch(full_path):
sampling_rate, data = read(full_path)
return torch.FloatTensor(data.astype(np.float32)), sampling_rate
def load_filepaths_and_text(filename, split="|"):
with open(filename, encoding="utf-8") as f:
filepaths_and_text = [line.strip().split(split) for line in f]
return filepaths_and_text
def get_hparams(init=True):
parser = argparse.ArgumentParser()
parser.add_argument(
"-c",
"--config",
type=str,
default="./configs/base.json",
help="JSON file for configuration",
)
parser.add_argument("-m", "--model", type=str, required=True, help="Model name")
args = parser.parse_args()
model_dir = os.path.join("./logs", args.model)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
config_path = args.config
config_save_path = os.path.join(model_dir, "config.json")
if init:
with open(config_path, "r", encoding="utf-8") as f:
data = f.read()
with open(config_save_path, "w", encoding="utf-8") as f:
f.write(data)
else:
with open(config_save_path, "r", vencoding="utf-8") as f:
data = f.read()
config = json.loads(data)
hparams = HParams(**config)
hparams.model_dir = model_dir
return hparams
def clean_checkpoints(path_to_models="logs/44k/", n_ckpts_to_keep=2, sort_by_time=True):
"""Freeing up space by deleting saved ckpts
Arguments:
path_to_models -- Path to the model directory
n_ckpts_to_keep -- Number of ckpts to keep, excluding G_0.pth and D_0.pth
sort_by_time -- True -> chronologically delete ckpts
False -> lexicographically delete ckpts
"""
import re
ckpts_files = [
f
for f in os.listdir(path_to_models)
if os.path.isfile(os.path.join(path_to_models, f))
]
def name_key(_f):
return int(re.compile("._(\\d+)\\.pth").match(_f).group(1))
def time_key(_f):
return os.path.getmtime(os.path.join(path_to_models, _f))
sort_key = time_key if sort_by_time else name_key
def x_sorted(_x):
return sorted(
[f for f in ckpts_files if f.startswith(_x) and not f.endswith("_0.pth")],
key=sort_key,
)
to_del = [
os.path.join(path_to_models, fn)
for fn in (x_sorted("G")[:-n_ckpts_to_keep] + x_sorted("D")[:-n_ckpts_to_keep])
]
def del_info(fn):
return logger.info(f".. Free up space by deleting ckpt {fn}")
def del_routine(x):
return [os.remove(x), del_info(x)]
[del_routine(fn) for fn in to_del]
def get_hparams_from_dir(model_dir):
config_save_path = os.path.join(model_dir, "config.json")
with open(config_save_path, "r", encoding="utf-8") as f:
data = f.read()
config = json.loads(data)
hparams = HParams(**config)
hparams.model_dir = model_dir
return hparams
def get_hparams_from_file(config_path):
# print("config_path: ", config_path)
with open(config_path, "r", encoding="utf-8") as f:
data = f.read()
config = json.loads(data)
hparams = HParams(**config)
return hparams
def check_git_hash(model_dir):
source_dir = os.path.dirname(os.path.realpath(__file__))
if not os.path.exists(os.path.join(source_dir, ".git")):
logger.warn(
"{} is not a git repository, therefore hash value comparison will be ignored.".format(
source_dir
)
)
return
cur_hash = subprocess.getoutput("git rev-parse HEAD")
path = os.path.join(model_dir, "githash")
if os.path.exists(path):
saved_hash = open(path).read()
if saved_hash != cur_hash:
logger.warn(
"git hash values are different. {}(saved) != {}(current)".format(
saved_hash[:8], cur_hash[:8]
)
)
else:
open(path, "w").write(cur_hash)
def get_logger(model_dir, filename="train.log"):
global logger
logger = logging.getLogger(os.path.basename(model_dir))
logger.setLevel(logging.DEBUG)
formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
if not os.path.exists(model_dir):
os.makedirs(model_dir)
h = logging.FileHandler(os.path.join(model_dir, filename))
h.setLevel(logging.DEBUG)
h.setFormatter(formatter)
logger.addHandler(h)
return logger
class HParams:
def __init__(self, **kwargs):
for k, v in kwargs.items():
if type(v) == dict:
v = HParams(**v)
self[k] = v
def keys(self):
return self.__dict__.keys()
def items(self):
return self.__dict__.items()
def values(self):
return self.__dict__.values()
def __len__(self):
return len(self.__dict__)
def __getitem__(self, key):
return getattr(self, key)
def __setitem__(self, key, value):
return setattr(self, key, value)
def __contains__(self, key):
return key in self.__dict__
def __repr__(self):
return self.__dict__.__repr__()
def load_model(model_path, config_path):
hps = get_hparams_from_file(config_path)
net = SynthesizerTrn(
# len(symbols),
108,
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers,
**hps.model,
).to("cpu")
_ = net.eval()
_ = load_checkpoint(model_path, net, None, skip_optimizer=True)
return net
def mix_model(
network1, network2, output_path, voice_ratio=(0.5, 0.5), tone_ratio=(0.5, 0.5)
):
if hasattr(network1, "module"):
state_dict1 = network1.module.state_dict()
state_dict2 = network2.module.state_dict()
else:
state_dict1 = network1.state_dict()
state_dict2 = network2.state_dict()
for k in state_dict1.keys():
if k not in state_dict2.keys():
continue
if "enc_p" in k:
state_dict1[k] = (
state_dict1[k].clone() * tone_ratio[0]
+ state_dict2[k].clone() * tone_ratio[1]
)
else:
state_dict1[k] = (
state_dict1[k].clone() * voice_ratio[0]
+ state_dict2[k].clone() * voice_ratio[1]
)
for k in state_dict2.keys():
if k not in state_dict1.keys():
state_dict1[k] = state_dict2[k].clone()
torch.save(
{"model": state_dict1, "iteration": 0, "optimizer": None, "learning_rate": 0},
output_path,
)
def get_steps(model_path):
matches = re.findall(r"\d+", model_path)
return matches[-1] if matches else None